Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Dynamic Regret of Adversarial Linear Mixture MDPs
Authors: Long-Fei Li, Peng Zhao, Zhi-Hua Zhou
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study reinforcement learning in episodic inhomogeneous MDPs with adversarial full-information rewards and the unknown transition kernel. We consider the linear mixture MDPs whose transition kernel is a linear mixture model and choose the dynamic regret as the performance measure. Denote by d the dimension of the feature mapping, H the length of each episode, K the number of episodes, PT the non-stationary measure, we propose a novel algorithm that enjoys an e O H4(K + PT )(1 + PT ) dynamic regret under the condition that PT is known, which improves previously best-known dynamic regret for adversarial linear mixture MDP and adversarial tabular MDPs. We also establish an Ω HK(H + PT ) lower bound, indicating our algorithm is optimal in K and PT . |
| Researcher Affiliation | Academia | Long-Fei Li, Peng Zhao, Zhi-Hua Zhou National Key Laboratory for Novel Software Technology, Nanjing University, China School of Artificial Intelligence, Nanjing University, China EMAIL |
| Pseudocode | Yes | Algorithm 1 POWERS-Fix Share; Algorithm 2 POWERS-Fix Share-On E |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. Thus, there is no mention of a public dataset or its accessibility. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, therefore it does not provide any training/validation/test splits. |
| Hardware Specification | No | The paper describes theoretical work and algorithm design; it does not report on empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper describes theoretical work and algorithm design; it does not report on empirical experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical experimentation. The section titled 'Problem Setup' describes the mathematical model, not a practical experimental configuration. No hyperparameters or system-level training settings are provided. |